Low-rank dimensionality reduction for multi-modality neurodegenerative disease identification
In this paper, we propose a novel dimensionality reduction method of taking the advantages
of the variability, sparsity, and low-rankness of neuroimaging data for Alzheimer's Disease …
of the variability, sparsity, and low-rankness of neuroimaging data for Alzheimer's Disease …
Nonlinearity-aware based dimensionality reduction and over-sampling for AD/MCI classification from MRI measures
Alzheimer's disease (AD) has been not only a substantial financial burden to the health care
system but also an emotional burden to patients and their families. Making accurate …
system but also an emotional burden to patients and their families. Making accurate …
Examing and Evaluating Dimension Reduction Algorithms for Classifying Alzheimer's Diseases using Gene Expression Data
S Li, P Yang, V Lanfranchi - 2021 17th International …, 2021 - ieeexplore.ieee.org
Alzheimer's disease (AD) is a neurodegenerative disease. Its condition is irreversible and
ultimately fatal. Researchers have been studying approaches to support early diagnosis of …
ultimately fatal. Researchers have been studying approaches to support early diagnosis of …
Sparse discriminative feature selection for multi-class Alzheimer's disease classification
In neuroimaging studies, high dimensionality and small sample size have been always an
issue, and it is common to apply a dimension reduction method to avoid the over-fitting …
issue, and it is common to apply a dimension reduction method to avoid the over-fitting …
High-order Laplacian regularized low-rank representation for multimodal dementia diagnosis
Multimodal heterogeneous data, such as structural magnetic resonance imaging (MRI),
positron emission tomography (PET), and cerebrospinal fluid (CSF), are effective in …
positron emission tomography (PET), and cerebrospinal fluid (CSF), are effective in …
Multi-modal dimensionality reduction using effective distance
D Zhang, Q Zhu, D Zhang - Neurocomputing, 2017 - Elsevier
By providing complementary information, multi-modal data is usually helpful for obtaining
good performance in the identification or classification tasks. As an important way to deal …
good performance in the identification or classification tasks. As an important way to deal …
Nonlinear dimensionality reduction combining MR imaging with non-imaging information
We propose a framework for the extraction of biomarkers from low-dimensional manifolds
representing inter-subject brain variation. Manifold coordinates of each image capture …
representing inter-subject brain variation. Manifold coordinates of each image capture …
Gaussian discriminative component analysis for early detection of Alzheimer's disease: A supervised dimensionality reduction algorithm
Background Using multiple modalities of biomarkers, several machine leaning-based
approaches have been proposed to characterize patterns of structural, functional and …
approaches have been proposed to characterize patterns of structural, functional and …
Exclusive feature selection and multi-view learning for Alzheimer's disease
J Li, L Wu, G Wen, Z Li - Journal of Visual Communication and Image …, 2019 - Elsevier
Abstract In Alzheimer's Disease (AD) studies, high dimension and small sample size have
been always an issue and it is common to apply a dimension reduction method to predict the …
been always an issue and it is common to apply a dimension reduction method to predict the …
A novel approach to perform linear discriminant analyses for a 4-way alzheimer's disease diagnosis based on an integration of pearson's correlation coefficients and …
B Mabrouk, AB Hamida, N Mabrouki, N Bouzidi… - Multimedia Tools and …, 2024 - Springer
Diagnosing Alzheimer's disease (AD) remains a significant challenge, particularly in
effectively identifying individuals in the early (EMCI) and late (LMCI) stages of Mild Cognitive …
effectively identifying individuals in the early (EMCI) and late (LMCI) stages of Mild Cognitive …